1. Field of the Invention
The present invention relates to a method and control system for angle estimation of sensor-less PM (permanent magnet) motors, and more particularly to a method and control system for angle estimation of brushless permanent magnet synchronous motor (PMSM).
2. Description of the Related Art
A permanent magnet synchronous motor (PMSM) includes a wound stator, a permanent magnet rotor assembly, and a sensing device to sense the rotor position. The sensing device provides signals for electronically switching the stator windings in proper sequence to maintain the rotation of the permanent magnet rotor assembly. The sensing device is a hall-sensor device in general. However, the hall-sensor device increases the cost of the permanent magnet synchronous motor and may cause the reliability problem. Therefore, a sensor-less control becomes a requirement for the PM motor control.
A permanent magnet (PM) motor offers advantages of high efficiency, small size, fast dynamic response, low noise, and high reliability, etc. The rotor field of the PM motor must be synchronous to the stator field, and, thus, field oriented control (FOC) represents the method by which one of the flux (such as rotor, stator, or air-gap) is considered as a basis for creating a reference frame for one of other flux with the purpose of decoupling the torque and flux producing components of the stator current. This means that the armature current is responsible for the torque generation, and the excitation current is responsible for the flux generation. Normally, the rotor flux is considered as a reference frame for the stator and air-gap flux. A control scheme of FOC is presented in FIG. 1. The FOC is a sensor-less FOC control system including a permanent magnet synchronous motor (PMSM) 10, a three-phase bridge driver (3-Phase Bridge) 15, and a space vector modulation module (SVM) 30. A Clarke transform module 20 is utilized to move a three-axis two-dimensional coordinate system (referenced to as the stator) onto a two-axis system.
It can be expressed as:ia+ib+ic=0iβ=ia iα=(ia+2×ib)÷√{square root over (3)}
where ia, ib, and ic are the individual motor phase currents. iα and iβ are two-axis orthogonal currents.
A Park transform module 25 is utilized to transform the two-axis orthogonal currents iα and iβ and the angle signal θ into another two-axis system that is rotating with the rotor flux. This two-axis rotating coordinate system is called d-q axis. The angle signal θ represents the rotor angle.Id=iβ×cos θ+iα×sin θIq=−iβ×sin θ+iα×cos θ
An inverse Park transform module 35 is utilized to transform from the two-axis rotating frame d-q to the two-axis stationary frame α-β.Vβ=Vd×cos θ−Vq×sin θVα=Vd×sin θ+Vq×cos θ
An inverse Clarke transform module (SVM) (also referred to as a space vector modulation module) 30 is utilized to transform from the two-axis stationary frame to the three-axis stationary frame (3-phase reference frame of the stator).Vp1=VβVp2=(−Vβ+√{square root over (3)}×Vα)÷2Vp3=(−Vβ−√{square root over (3)}×Vα)÷2
These 3-phase (Vp1, Vp2, Vp3) are applied to generate pulse-width modulation signals, e.g. the space vector modulation (SVM) techniques.
Controllers (PI) 40 and 45 are proportional integral (PI) controllers. Each of the controllers 40 and 45 responds to an error signal in a closed control loop and attempts to adjust the controlled quantity to achieve the desired system response. The controlled parameter can be measurable system quantity such as speed, torque, or flux. The error signal is formed by subtracting the desired setting of the parameter to be controlled from the actual measured value of that parameter. The sign of the error signal indicates the direction of change required by the control input.
A sliding mode observer (SMO) 50 is used for the angle signal θ and speed estimation. FIG. 2 and FIG. 3 show a system block and an algorithm of an example of the sliding mode observer 50. The important part of the algorithm is how to calculate the commutation angle signal θ needed for the FOC. The motor position is estimated based on the measured currents and the calculated voltages. FIG. 4 shows a motor model for the PMSM 10. The motor model includes an input voltage VS that is applied to the motor composed of a winding resistance R, a winding inductance L and a back-EMF (back-electromotive force) (ES) 12. Thus, a current observer 60 in FIG. 2 and FIG. 3 can be expressed as
            ⅆ              (        Ise        )                    ⅆ      t        =                              -          R                L            ×      Ise        +                  1        L            ×              (                  VS          -          ES          -          Z                )            
where IS is the motor phase current, Ise is the estimated phase current, VS is the input voltage, ES is the back-EMF, and Z is the output correction factor voltage.
Considering two motor representations, the same input voltage VS fed into both systems, and the measured motor phase current IS matched with the estimated phase current Ise from the model, we can presume the back-EMF ES from the motor model is the same as the back-EMF ES from the motor. When the error value (Error/IS Error) between the measured motor phase current IS and the estimated phase current Ise is lesser than a threshold Error-min, then the current observer 60 works in the linear range. For an error outside of the linear range, the output of the current observer 60 is (+Kslide)/(−Kslide) depending on the sign of the error value. The current observer 60 is utilized to compensate the motor model and estimate back-EMF ES by filtering (via a filter 71, such as a low pass filter (LPF)) the output correction factor voltage Z. The estimated back-EMF ES is further coupled to generate the values (ESF) of Eα and Eβ (vector components of ES) through a filer 72 (such as a low pass filter (LPF)) for the estimated angle signal θ calculation (80).
Because the SMO (sliding mode observer) 50 requires the motor's parameters and complex calculations for the estimation of the commutation angle signal θ, thus a high-speed and expensive DSP (digital signal process) is required for the control.